Biology subjects

Yang, J. Y. H.

Publications and source records attributed to Yang, J. Y. H..

3 recordsLinked to original sources

bioRxiv2018

Multi-Omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency

Pluripotency is highly dynamic and progresses through a continuum of pluripotent stem-cell states. The two states that bookend the pluripotency continuum, naive and primed, are well characterized, but our understanding of the intermediate states and transitions between them remain incomplete. Here, we dissect the dynamics of pluripotent state transitions underlying pre-to post-implantation epiblast differentiation. Through comprehensive mapping of the proteome, phosphoproteome, transcriptome, and epigenome of mouse embryonic stem cells transitioning from naive to primed pluripotency, we find that rapid, acute, and widespread changes to the phosphoproteome precede ordered changes to the epigenome, transcriptome, and proteome. Reconstruction of kinase-substrate networks reveals signaling cascades, dynamics, and crosstalk. Distinct waves of global proteomic changes demarcate discrete phases of pluripotency, characterized by cell-state-specific surface marker expression. Our data provide new insights into the multi-layered control of the phased progression of pluripotency and a foundation for modeling mechanisms underlying pre-to post-implantation epiblast differentiation.\n\nHIGHLIGHTSO_LIMulti-ome maps of cells transitioning from naive to primed pluripotency\nC_LIO_LIPhosphoproteome dynamics precede changes to epigenome, transcriptome, and proteome\nC_LIO_LIKinase-substrate network reconstruction uncovers signaling dynamics and crosstalk\nC_LIO_LIProteins and cell surface markers that track pluripotent state transitions\nC_LIO_LIComparative analysis of mouse and human pluripotent states\nC_LI

genomics↗

bioRxiv2018

scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication

Concerted examination of multiple collections of single cell RNA-Seq (scRNA-Seq) data promises further biological insights that cannot be uncovered with individual datasets. However, such integrative analyses are challenging and require sophisticated methodologies. To enable effective interrogation of multiple scRNA-Seq datasets, we have developed a novel algorithm, named scMerge, that removes unwanted variation by combining stably expressed genes and utilizing pseudo-replicates across datasets. Analysis of large collections of publicly available datasets demonstrates that scMerge performs well in multiple scenarios and enhances biological discovery, including inferring cell developmental trajectories.

bioinformatics↗

bioRxiv2018

DCARS: Differential correlation across ranked samples

Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform patient prognosis called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the classification of patients into good or poor prognosis groups and can identify differences in gene correlation across early, mid or late stages of survival outcome. When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across a number of cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing a number of distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multilayered and complex data.\n\nAvailability: https://github.com/shazanfar/DCARS.

bioinformatics↗